iprofile               package:rmutil               R Documentation

_P_r_o_d_u_c_e _I_n_d_i_v_i_d_u_a_l _T_i_m_e _P_r_o_f_i_l_e_s _f_o_r _P_l_o_t_t_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     'iprofile' is used for plotting individual profiles over time for
     objects obtained from dynamic models. It produces output for
     plotting recursive fitted values for individual time profiles from
     such models.

     See 'mprofile' for plotting marginal profiles.

_U_s_a_g_e:

     zz <- iprofile(z, plotsd=FALSE)
     plot(zz, nind=1, observed=TRUE, intensity=F,
             add=FALSE, lty=NULL, pch=NULL, ylab=NULL, xlab=NULL,
             main=NULL, ylim=NULL, xlim=NULL, ...)

_A_r_g_u_m_e_n_t_s:

       z: An object of class 'recursive', from 'carma', 'elliptic',
          'gar', 'kalcount', 'kalseries', 'kalsurv', or 'nbkal'.

      zz: An object of class 'iprofile'.

  plotsd: If TRUE, plots standard deviations around profile ('carma'
          and 'elliptic' only).

    nind: Observation number(s) of individual(s) to be plotted.

observed: If TRUE, plots observed responses.

intensity: If z has class, 'kalsurv', and this is TRUE, the intensity
          is plotted instead of the time between events.

     add: If TRUE, the graph is added to an existing plot.

  others: Plotting control options.

_V_a_l_u_e:

     'iprofile' returns information ready for plotting by
     'plot.iprofile'.

_A_u_t_h_o_r(_s):

     J.K. Lindsey

_S_e_e _A_l_s_o:

     'carma', 'elliptic', 'gar', 'kalcount', 'kalseries', 'kalsurv',
     'nbkal' 'mprofile' 'plot.residuals'.

_E_x_a_m_p_l_e_s:

     library(repeated)
     times <- rep(1:20,2)
     dose <- c(rep(2,20),rep(5,20))
     mu <- function(p) exp(p[1]-p[3])*(dose/(exp(p[1])-exp(p[2]))*
             (exp(-exp(p[2])*times)-exp(-exp(p[1])*times)))
     shape <- function(p) exp(p[1]-p[2])*times*dose*exp(-exp(p[1])*times)
     conc <- matrix(rgamma(40,1,scale=mu(log(c(1,0.3,0.2)))),ncol=20,byrow=TRUE)
     conc[,2:20] <- conc[,2:20]+0.5*(conc[,1:19]-matrix(mu(log(c(1,0.3,0.2))),
             ncol=20,byrow=TRUE)[,1:19])
     conc <- ifelse(conc>0,conc,0.01)
     z <- gar(conc, dist="gamma", times=1:20, mu=mu, shape=shape,
             preg=log(c(1,0.4,0.1)), pdepend=0.5, pshape=log(c(1,0.2)))
     # plot individual profiles and the average profile
     plot(iprofile(z), nind=1:2, pch=c(1,20), lty=3:4)
     plot(mprofile(z), nind=1:2, lty=1:2, add=TRUE)

